Resumen de Non-destructive detection of dicyandiamide in infant formula powder using multi-spectral imaging coupled with chemometrics.
Changhong Liu, Wei Liu, Jianbo Yang, Ying Chen, Lei Zheng
Dicyandiamide (DCD) contamination of milk and milk products has become an urgent and broadly recognised topic as a result of several food safety scares. This study investigated the potential of using multi-spectral imaging (405-970 nm) coupled with chemometrics for detection of DCD in infant formula powder. Partial least squares (PLS), least squares-support vector machines (LS-SVM), and back-propagation neural network (BPNN) were applied to develop quantitative models.; Results: Compared with PLS and LS-SVM, BPNN considerably improved the prediction performance with coefficient of determination in prediction (RP2) = 0.935 and 0.873, residual predictive deviation (RPD) = 3.777 and 3.060 for brand 1 and brand 2 of infant formula powders, respectively. Besides, multi-spectral imaging was able to differentiate unadulterated infant formula powder from samples containing 0.01% DCD with no misclassification using BPNN model.; Conclusion: The study demonstrated that multi-spectral imaging combined with chemometrics enables rapid and non-destructive detection of DCD in infant formula powder. © 2016 Society of Chemical Industry.; © 2016 Society of Chemical Industry.
